End-to-End Optimized Versatile Image Compression With Wavelet-Like Transform

Built on deep networks, end-to-end optimized image compression has made impressive progress in the past few years. Previous studies usually adopt a compressive auto-encoder, where the encoder part first converts image into latent features, and then quantizes the features before encoding them into bi...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 44; H. 3; S. 1247 - 1263
Hauptverfasser: Ma, Haichuan, Liu, Dong, Yan, Ning, Li, Houqiang, Wu, Feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Bit rate
Coders
Coefficients
Compression ratio
Deep network
end-to-end optimization
Entropy coding
Image coding
Image compression
lossless compression
lossy compression
Mathematical models
Measurement
Quantization (signal)
Rate-distortion
wavelet transform
Wavelet transforms
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Zusammenfassung:Built on deep networks, end-to-end optimized image compression has made impressive progress in the past few years. Previous studies usually adopt a compressive auto-encoder, where the encoder part first converts image into latent features, and then quantizes the features before encoding them into bits. Both the conversion and the quantization incur information loss, resulting in a difficulty to optimally achieve arbitrary compression ratio. We propose iWave++ as a new end-to-end optimized image compression scheme, in which iWave, a trained wavelet-like transform, converts images into coefficients without any information loss. Then the coefficients are optionally quantized and encoded into bits. Different from the previous schemes, iWave++ is versatile : a single model supports both lossless and lossy compression, and also achieves arbitrary compression ratio by simply adjusting the quantization scale. iWave++ also features a carefully designed entropy coding engine to encode the coefficients progressively, and a de-quantization module for lossy compression. Experimental results show that lossy iWave++ achieves state-of-the-art compression efficiency compared with deep network-based methods; on the Kodak dataset, lossy iWave++ leads to 17.34 percent bits saving over BPG; lossless iWave++ achieves comparable or better performance than FLIF. Our code and models are available at https://github.com/mahaichuan/Versatile-Image-Compression .
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2020.3026003